1. Field of the Invention
This invention generally relates to warehouse management systems, and more particularly, to a method and system to project required rack space based on physical dimensions and inventory peaks.
2. Background Art
Warehouses can be divided into several categories according to use. For instance, warehouses may be used within one company for storing goods for daily sales, and other warehouses may be used in factories for storing stock materials and manufactured products for retailing. Other warehouses may be used for temporary storage of import/export goods at an airport or seaport, while other warehouses may be located in an industrial or factory area for use by nearby companies.
In the past, warehouse facilities were simple, and materials were simply categorized and stored in the warehouse and retrieved on demand. Nowadays, along wish the development of international trade and the growth in the size of manufacturers, warehouses have become much more complex and, in particular, are now used to hold a large variety of incoming and outgoing goods and materials.
Many companies have built large and automated warehouses that are used so as to adjust the supply of raw materials and to prevent excess stacks that may affect company operations. Warehouses may also be managed so as to avoid a lack of raw materials on the production line, which would adversely affect product output.
In a traditional warehouse, arriving materials and goods are checked to determine if they are consistent with order request forms, receipts and other documents, and the materials and goods m&y be inspected to determine if they are damaged and if they comply to correct specifications. The incoming and outgoing materials and goods may be recorded, and the goods and materials may be stored in predetermined positions. After delivery, goods may be re-ordered, and regular stocktaking may be performed to ensure that the stocks comply with records.
One of the most time consuming and costly operations in a warehouse is ‘Order picking’—the selection of items from their warehouse storage locations to fill customer orders. Several factors such as (i) demand pattern of the items, (ii) configuration of the warehouse, (iii) location of the items in the warehouse, and (iv) picking method of retrieving the items contribute to the efficiency of this operation.
Warehousing and distribution centers operations are historically one of the most frequently overlooked and inadequately planned corporate functions. Among these functions, order picking is the single largest expense in most warehouses, accounting for approximately 65% of the operational costs. Moreover, order picking has a significant impact on the cycle time of the process. When there is a requirement/customer order, the order picker has to perform the following activities—(i) travel to the pick location, (ii) search for the item, (iii) retrieve the item and (iv) return to the work location. Among these activities, search and retrieving accounts for about 40% of the total time, whereas about 55% of the time is spent traveling. Hence, improving the order picking process would have a significant impact on the operational expense of a warehousing operation, in addition to the cycle time benefits.
Today's manufacturers, facing the intensifying competition and steady pressure for higher levels of customer service, are compelled to continuously improve their supply chain management. Most of these manufacturers use the production control philosophy that combines build-to-plan with make-to-order operations, commonly referred to as the fabrication/fulfillment process. The fabrication stage is a build-to-plan process, where components are procured, tested, assembled, and then kept in stock ready for due final assembly into the end-products. The fulfillment stage is a make-to-order process, which, means that no finished goods inventory is kept for end-products and the final assembly starts after the customer order is received.
When using such a model for the business operations, it is extremely difficult to manage the warehouse from a logistical and physical layout standpoint. Numerous constraints such as part shortages and uncertain demands are present making the process extremely cumbersome to model. The travel time can be minimized by identifying the ‘best’ location for the parts to be placed in a warehouse. In addition to the location of a part, the warehouse managers should be able to determine the space allocation for a specific part, based on demand and supply.
Today's warehouses have to frequently execute customized transactions, handle and store more products, offer more product and service customization, and provide more value added services. However, these warehouses have very minimal time to process the orders with almost no margin for error. Numerous warehouses try to solve these challenges by implementing additional technology. However, this strategy could complicate the situation even further. Literature shows that a significant contributor to the complications in the warehouse is the lack of an effective slotting strategy. Most warehouses may be spending 10-30 percent more per year than they should, since it is estimated that less than 15 percent of the SKUs (stock-keeping units) are properly slotted.
Warehouse management is an extensively researched area from both process improvement and logistical viewpoints. There is very limited literature, though, on order picking and warehouse layout strategies. Although the existing literature talks extensively about popularity of parts, turnover and cube-per-order index, none of them specify the logic used to allocate the parts in each slot. Popularity, turnover, and cube-per-order index (COI) may perform best among slotting measures.
The most commonly used slotting strategies are as below:    1. Popularity—It is the number of picks per day, or the part velocity.    2. Turnover—The demand of a product at any time is called the turnover.    3. Volume—The product of the demand and the volume (cubic) of a product.    4. Pick Density—It is the ratio of the popularity to the cubic volume.    5. Cube-Per-Order Index (COI)—It is the ratio of the cubic volume of s part to its turnover.
The other related work in optimizing a layout focuses on identifying methods to determine the distance traveled by the operator in the warehouse. Many researchers model this as a traveling salesman problem. This work focused on reducing the picking time and not on the actual location of the parts.
Yet another area that has been researched is the method to reduce the order picking time based on volumes or turnover storage policies. In such methods, the parts with the highest number of picks were close to the front of the picking zones. Although this philosophy is very critical and effective, no method to actually determine the highest number of picks has been documented. Also, these models use static information to determine the number of picks and other attributes. This is very ineffective in a fabrication-fulfillment environment with constantly changing product demands as well as designs.